Outbrain released a study in April on the referral sources driving traffic to content pages, in terms of percent of referrals and the quality of the referred traffic. They describe the dataset as 100 million sessions from over 100 premium publishers using their services.

When traffic quality is assessed by page views per session, search is the strongest traffic source followed closely by content sites with portals and social networks trailing. Looking at an inverse measure, bounce rate, social networks and portals show the most one-page-and-out behavior, followed by content sites and then search…the least bouncy. Looking at hyper-engaged sessions, defined as 5 pages or more, content sites lead, followed by search with portals and social networks trailing.

So…it would seem that social-media driven traffic is less engaged than traffic from other sources. But I think there’s more going on here than meets the eye, as I’ll note on the other side of these charts from the Outbrain report.

I don’t think this finding is really about loyalty. It’s about how specifically each of these traffic sources drives users to the exact content within the site that’s of interest to them.

When people enter content pages through search they may be searching for the site per se, using search as a proxy for typing in the URL. They’re likely to come through the front door of the site, the home page. Multiple pages per session through search may not indicate loyalty but rather a fumbling about trying to find the specific content on the site that’s of interest. In contrast, when they come through social networks they are being directed to a very specific piece of content referred by a friend…they go directly there, read it and bounce away.

The site experience may, in fact, be superior for social networks even though the loyalty metric looks weak. And it may be a desired outcome for the publisher if that one page is deeper in the site and commands higher premiums than more generic content near the front. Not all page views are created equal. The value of social networks may not be sheer tonnage of page views generated but rather more targeted page views…driving visitors more directly to the pages that the publisher most wants visitors to consume.

Though loyalty (or lack thereof) looks the same for social networks and for portals, I would bet there’s a critical difference. When visitors go one-page-and-out after being referred by a portal they are likely bouncing off the home page. When they go one-page-and-out after being referred by a social network they’re likely bouncing off a specific post or article, some of the site’s more premium content.

So it was interesting for me to find this CNET item from November 2010 citing a comment from Netflix CEO Reed Hastings: the tablet craze affects us very little:

“People prefer large screens,” Hastings said. “So the impact of Xbox, PS3, the Wii phenomenon–huge impact. The impact of the iPad–it’s a great system, but the Mac laptops outstrip the iPad for Netflix viewing by a huge factor.” Long-form video viewing does not translate that well to mobile platforms, he asserted.

So this downplays the impact of the iPad, from a company whose strategy is driving the trends; they would be in a position to know. Though it suggests that the 4-foot screen, in the form of Mac laptops, is a major outlet for Netflix product.

I’d love to see data on number of streams and time spent with online video broken down by screen type – current state and recent growth. My hypothesis: the 4-foot screen dominates current online video consumption by far, but dominates less as length of stream increases, with the 10-foot screen being relatively stronger for longer form and the driver of long-form growth.

Though another paragraph in the CNET article suggests that this hypothesis may be U.S. centric, perhaps true only for a culture in which the TV is the center of life:

Former News Corp. executive Peter Chernin, who joined Hastings on the panel, said he agrees, with regard to the U.S. market, but that the story will be very different in developing markets, where big-screen TVs are less commonplace and cheap tablet devices will soon be readily available.

Poltrack contrasted Netflix’s remarkable growth with rather tepid adoption rates of other so-called “over-the-top” TV streaming platforms, such as GoogleTV, Boxee and AppleTV, but said that video game platforms such as Microsoft’s Xbox, Nintendo’s Wii, and Sony’s PS3 have become a major means of streaming TV programming, and that many of those platform users are actually doing so via Netflix.

So…it’s a box that already exists in the household, that’s already connected to the TV for some other purpose that is serving to-date as the main over-the-top conduit. And that makes some basic human sense.

One of the things I’ve noted across…let’s just say years of analyzing different types of data is the degree to which data silos can obscure the ability to see what’s really going on in the world. By data silos, I mean the set categories of information that people who analyze and report information tend to organize around…and the categories that those who make use of the information expect to see.

There’s an example in my post: Is Your Video Traffic Upside Down? In general, site dashboards are set up to show traffic trends (what’s happening with page views) and video trends (here are the video starts, completes, etc.). But unless you put one in context of the other (as per the post) you won’t see the interesting patterns that reflect how people consume video relative to text…by day of week, time of day, etc.

Data silos run rampant when the results of survey research are reported. They’re often reported by question, the answers to question 1 followed by the answers to question 2. When the real insight is sometimes only apparent when you put the answers to question 1 next to the answers to question 17, or 21…or some other context that tells the story. In general, I think the best practice is to isolate the themes, the learning, what do the results of the survey say? And then pull in data to support those themes in whatever order makes conceptual sense rather than the order of the questionnaire.

Different sources of information can become data silos. In the digital world, editorial people look at traffic data and ad sales people look at revenue, impressions and CPM data. But the reality of the site and its business can only be truly understood by looking at the two sources in tandem. In the world of TV networks, ratings, revenue data and brand strength data from survey research may seem like three disparate pieces of a puzzle. But when you put the three together there are stories to be told…

I’m not recommending that we throw everything into one pile and make a great big data soup. Hypotheses about consumer behavior and market dynamics have to be used as a lens to focus how and where we slice the information.

But I think, to large degree, the process of analysis is a process of breaking the silos of how the data was obtained and reclassifying it along the lines of what it says about the consumer and the marketplace.

Essentially, the sense we make of the world reflects the categorical structure we use to see it…

And the essential job of the data analyst is to break the artificial, ingoing structures and build the one that best approximates reality…out there in the world.

A J.D Power and Associates study released yesterday shows that just 3% of residential TV customers have cancelled their multichannel subscriptions, 6% among Generation Y customers. I’d argue that 6% among the younger audience is not insignificant; it’s a factor. But those levels won’t be turning the TV world upside down.

This may sound odd, but I think the most important aspect of cord cutters not that they’re cutting the cord with their multichannel providers. The most important thing is they’re likely to be driving online video over-the-top to their 10-foot screens. The cord cutting aspect of the cord cutters…may be misdirection with regard to future trends.

To clarify terms, when I say “over-the-top” I mean streaming online video to a TV set rather than a computer or mobile screen and watching in lean-back mode, the way people traditionally watch TV.

Because over-the-top streaming is initially focused among the young, the tech-savvy, lower incomes, light TV viewers…it is more likely to replace traditional TV for these audiences. But, as over-the-top becomes mainstream, only a minority of people will actually cut the cord. Thus the key scenario, the one that will eventually play out for the majority of households, is that both traditional and over-the-top TV will compete for time in the 10-foot, traditional TV viewing experience.

I think some basic, human factors will drive to this scenario:

People will always prefer to watch long form programming on the 10-foot screen. Watching full-length TV shows and movies on 4-foot and 2-foot screens will increase; the iPad will help drive its growth. But the average length of an online video stream is still under 5 minutes. I’d argue that the average length of online streams won’t really take off till the mainstream consumer is sending online video over-the-top to his TV.

Over-the-top will penetrate the home through the path of least resistance. That path is the Internet-connected TV; the least number of extra boxes and wires entailed. Blu-Ray players and Internet-connected game consoles will be secondary; boxes people already own for other purposes. This path mitigates against cord-cutting; why buy a spanking new TV and then cut down your viewing options? It also ties the rate of behavior change to TV replacement cyles and the briskness (or not) of new TV sales.

Network programming will continue to alleviate the burden of choice. Lighter TV viewers may be able to subsist entirely on a diet of on-demand viewing. But for heavier TV viewers it would be onerous to deliberately select everything they watch. Channel surfing is a key part of their diet that can be supplemented but not entirely replaced with on-demand options. And so…as over-the-top migrates to heavier TV viewers, a pattern of coexistence rather than replacement will be seen.

Here’s an interesting data point from the Leichtman Research Group. 30% of all households report having at least one TV connected to the Internet via a video game system, a Blu-Ray player and/or the TV itself. But only 10% of all adults watch video from the Internet via one of these devices at least weekly. So a substantial number of households already have the capability; most don’t use it regularly. Of course the numbers are much higher among Netflix subscribers…

When people have both forms of TV, traditional and over-the-top, available to them in lean-back mode, what do they do, what trade-offs do they make, what gets cannibalized? If I were a network executive, this future scenario would concern me the most.

Yahoo/Interpret recently published results of an online video study updating a study conducted in 2009. One of the findings is that online video, which previously peaked mid-day, now peaks in the evening hours; the Yahoo report refers to “online primetime”. As per their key chart, cited below…

From "Phase 2 Of Video Evolution Revolution", Yahoo/Interpret, 6/2011

This startled me because of both the similarities and differences versus my post of a couple of days ago, Is Your Video Traffic Upside Down. I argued, for major content sites with a mix of text and video, that people are relatively more prone to watching video on the weekends and in the evenings when total traffic for these sites is at low ebb. The Yahoo data shows that people are consuming more video in the evenings on an absolute basis.

Of course I was talking about content sites with a mix of text and video while the trend cited above is driven by Hulu and Netflix, video-focused sites. That may be the difference. I also wonder, this being self-reported data, whether evening online viewing is more deeply engaged in and therefore better remembered, more likely to be reported in a survey.

But the key data point that that this chart seems to beg for: how much of total “online” video content is being watched on a computer screen versus how much is being ported to a TV screen?What’s the trend for this?

What’s of interest to me is how much online viewing is in typical TV lean-back manner, on a big screen 10-feet or so away…versus on a small screen 2-feet or so away.

Because I would think the “online primetime” trend and a “online 10-foot viewing trend” would be happening in tandem.

Much has been written about the Nielsen position on cord cutting…that it is, in fact, happening, but among segments that are not of core importance to the TV industry. A key paragraph from the June 2010 Nielsen press release below:

“….shifting to online video mainly appears to be happening in small pockets of the population, including young, emerging households. Households with no cable subscriptions at all, but who subscribe to a broadband service, also reflect a younger population of college graduates and lower to middle income consumers who may not be fully convinced of the need to pay for digital cable. However, Nielsen data shows that these individuals are typically light TV viewers who watch 40% less TV per day than the national average. And while they stream about twice the average amount of video, they still only stream about 10 minutes per day, hardly an indication of a monumental shift to online-only viewing.”

The delicious irony of this position: according the the theory of disruptive innovation (Clayton Christensen, The Innovator’s Dilemma, 2003) it is precisely among non-core audiences that disruptive innovations gain their initial foothold. Check out this quote from The Innovator’s Dilemma:

“First, disruptive products are simpler and cheaper; they generally promise lower margins, not greater profits. Second, disruptive technologies are typically first commercialized in emerging or non-significant markets. And third, leading firms’ most profitable customers don’t want, and indeed initially can’t use, products based on disruptive technologies. By and large, a disruptive technology is initially embraced by the least profitable customers in a market…”

So…according to the Nielsen analysis, disruption of the TV business is proceeding more-or-less in textbook fashion. Not what they intended I’m sure.

One niggle to applying the concept of disruptive innovation to cord cutting: disruptive innovations generally deliver a lower quality product (offset by other benefits). At least at first. While most cord cutters would likely argue that the quality of their viewing experience through their over-the-top devices is quite as good as they would get from multi-channel providers.

I’d argue, though, that cord cutters are sacrificing ease-of-use. Not every mainstream consumer would know what over-the-top device to get or how to hook it up. Lack of knowledge and a hassle factor keeps the disruption isolated among demographic pockets with greater economic need and some technological savvy…who are willing to go through a little trouble.

Will exclusive content, only available through multi-channel subscriptions, become a critical barrier against the increased penetration of cord cutting? Live sports, in particular, may be a key factor that keeps people from cutting the cord so long as there is no easy/legal way to get major sports events over-the-top. If this is true we may see female-headed households and those who care less about sports become the next demographic frontier for cord cutting.

Is there a psychological barrier for a significant portion of TV viewers (particularly heavy viewers) against the on-demand only type of experience that cord cutting entails? To the degree that there is an inherent need to surf channels…there may be an anti-cord cutting barrier for heavy viewers.

Will the TV Everywhere initiatives of the major TV players help forestall disruption of their business?

It will be fascinating how it all plays out…

But one thing we must not do is dismiss the phenonomenon based on its initial audience characteristics. Quite the contrary.

For the content sites I’ve worked on there’s often been the following mantra on the business and editorial teams:

We want to our site to be part of our users’ lives. We may be a quick, entertaining mid-day mental snack, but one our users can’t live without. We want them to come back (practically) every day.

But there have been a number of times that I’ve run into the following pattern…a pattern that makes me wonder about the soundness of this goal. I’ve reproduced it here using Quantcast data that’s in the public domain, starting with site ranked number 22 (Huffington Post) so as to exclude the very top-ranked utilities and running down to number 89, excluding sites that are not quantified or block access to their frequency data. There are 32 sites on the chart – the point is that I wasn’t cherrypicking.

Quantcast Loyalty Data For 32 Highly-Ranked Sites - 6/28/2011

The chart breaks the number of pages per monthly visitor into two components, page views per visit across the x axis and visits per visitor across the y. These are the two components of loyalty, how deep do people go into the site when they visit and how often they come to the site.

The striking thing is that all the sites fall into a narrow range on the visits/visitor axis. Almost all fall between an average of 1 and 4 visits per month. Of the 32 sites there are a couple of outliers..to all of about 4.5 and 5.5 visits per visitor per month (those would be Tumblr and Drudge Report). The pages per visit axis has the wider range, with the majority of sites in the 1-10 range and outliers into the low teens, one into the 20’s (Tumblr, 4share and Drudge Report).

The admitted flaw here is that I’m working with averages. If you break down the average of 1-4 visits a month and look at the distribution of visits across visitors there are sure to be segments that visit the site every day, several times a day.

But my bet, looking at the averages, is that those highly loyal groups, in terms of visit frequency, will tend to be small, perhaps infinitesmal. For the most part the online world is so noisy, there are so many options at people’s disposal, that it is difficult to generate visit frequencies to content sites of more than once a week or so. The bigger driver of loyalty, the measure that creates more of a difference between the more sticky and more bouncy content sites, is how deep visitors go on each visit. I believe the pattern implies…at least for content sites…that it is the pages/visit aspect of loyalty, the within session aspect, that deserves relatively more analytical and editorial focus.

But there’s another headscratch, aside from how different the numbers are. Each source tells a totally different story about where the growth in online video is coming from. Completely different answers to the question: more users or more usage?

Take these Jan 2011 Nielsen numbers for example. They seem to say that the audience for online video is saturated and growth comes almost entirely from dramatically more usage from the same people. I calculated a number from those provided that suggests: growth in time spent comes from more videos per person; not so much from increased time per stream:

Nielsen Online Video Data: January 2011

Now Comscore, for Dec 2010, from their 2010 Digital Year In Review:

Comscore Online Video Data: December 2010

Granted the measures are apples and oranges. Still, the Comscore data seems to say that online video users are increasingly dramatically while usage growth is more modest. A completely different story.

With no bias toward sources or methodologies, the Nielsen growth story is the one I tend to believe. I would think, at this point in its evolution, most everyone who’s going to watch online videos is already doing so (not counting new users coming in as kids mature). I would guess that growth is coming from changing behavior among those who’ve already caught the habit.

Clearly there’s tremendous usage upside for online video. If you think about the higher (Comscore) number for online video consumption, 14.2 hours a month, and compare it to consumption of TV, at some 35 hours a week. Given that comparison you’d sort of expect usage to be the dynamic driving online video growth.

The question for either story is the detail behind it:

If new users are pouring into online video, as per Comscore, who are those users; what are their demographics or other characteristics?

If the same users are consuming dramatically more streams as per Nielsen, how are there habits changing? What types of video content are making up the difference?

And why do both sources show the average length of stream to be so short (both under 5 minutes) and growing relatively modestly compared to the other metrics? Wouldn’t we expect, given the growth of Netflix, for this measure to be growing dramatically?

I would love to break this information down into user segments. Because I would bet there are segments of consumers for whom average length of stream is much longer than the average and segments for whom this metric is growing more sharply than the others. When those segments start to drive the total sample average, when average length of stream really starts to grow, that’s when disruption of the TV business will be under way.

For content sites with a mix of text and video, are there times when it makes sense to shift the balance towards video?

I would argue, for these types of sites, video traffic tends to be upside down. Video consumption will be relatively lower during the site’s peak traffic hours, toward the middle of the day on weekdays. Video consumption will be relatively higher on weekends and in the evenings when total site traffic is at low ebb.

The way to see the pattern is with a calculated metric, video starts/page views. Call it the video content ratio. There are issues with this metric, as I’ll note in a couple of paragraphs. But it indicates a rough percentage of the content consumption on your site accounted for by video, a useful thing to know. And you can track it across time to see interesting patterns in video consumption, as related to total site traffic.

What you will likely see, if you go through this exercise, is that page views and video starts both tend to peak during the week versus the weekend and they both tend to peak during the mid-day hours versus the evening. But video will peak less sharply and trough less, so that the video content ratio will actually peak on the off days and off hours.

The implied consumer behavior behind the pattern is the most interesting thing. During peak traffic times when people are at work they’re looking for information in quicker, tighter hits. They’re consuming everything in high quantities, text and video, but there there are inherent efficiencies for text. During soft traffic times when people are more likely at home they’re less likely to be online and cruising content sites. But when they are using content sites they are more in lean-back mode… and in a more conducive mood for watching videos. Perhaps somewhat longer videos as well.

There may be a number of ways for site programming to take advantage of this picture…

Before leaving the topic, a few words on the video content ratio. What’s wrong with this, aside from being yet one more calculated metric, are the various fudge factors. Are there auto starts on various pages? That will distort the ratio. Does the implementation count some video starts as page views; are these completely exclusive definitions or is there some fuzzy overlap? So there are certainly issues…let’s call it crude.

And yet there are various uses, as you can see. It’s helpful, when looking at video trends for a site to benchmark them against total traffic trends, to see if video is merely trending with the site or on a distinct trajectory. It’s useful, when there are a number of sites in a portfolio, to see which are doing relatively better in delivering video content relative to each traffic base. Accepting its warts, the video content ratio is a way to break down data silos and look at the big picture. And that’s always a good thing.